Workforce management (“WFM”) solutions are often utilized to capture data from automatic call distribution (“ACD”) systems on call volumes in each of a number of queues in a contact center. The queues may be associated with a particular skill. This, in turn, can be used to predict skills needed for future staffing of the contact center. As long as a work item is assigned to a, “correct skill(s) needed” queue to start, then “true” statistics can be collected from that queue on the “true” needs of incoming items.
In a contact center with a work assignment engine (“WAE”), work items may be held in an incoming work queue. Skill requirements may be assigned to the incoming work item, and so once again, statistics can be collected on each work item, and skills needed in the future staffing can be predicted.
A problem emerges when incoming work does not have all skills assigned. An example would be where incoming work item needs the skill, “reservations,” but when looking up the customer relation management (“CRM”) data for the customer, it is determined that the customer speaks, and prefers, to interact with agents in French. As the WAE assigns agents to work items, the WAE may find that in order to best serve the customer, the customer should be assigned to an English-only speaking agent. The English-only speaking agent processes the work item and the associated statistics show that work item as being successfully processed by a the English speaking agent.
In another situation, there are no, “hard skill” requirements, but rather soft or proportional requirements, such as when the skills needed are represented as a value function, and the WAE attempts to maximize the value of each assignment. Utilizing the example above, the function might assign 100 points to assigning the work item to a reservation agent, and an additional 30 points to a French capable agent, 10 points to English speaking agent, and −100 points to any other language. As the algorithms execute, there will be a strong bias to get the call to a reservation agent, some bias to get to French over English speaking agent, and a strong bias against other languages.